The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 17, 2026
Filed:
Jul. 13, 2023
Oracle International Corporation, Redwood Shores, CA (US);
Aashna Devang Kanuga, Foster City, CA (US);
Cong Duy Vu Hoang, Wantirna South, AU;
Mark Edward Johnson, Sydney, AU;
Vasisht Raghavendra, San Mateo, CA (US);
Yuanxu Wu, Foster City, CA (US);
Steve Wai-Chun Siu, Melbourne, AU;
Nitika Mathur, Melbourne, AU;
Gioacchino Tangari, Sydney, AU;
Shubham Pawankumar Shah, Foster City, CA (US);
Vanshika Sridharan, San Mateo, CA (US);
Zikai Li, Redwood City, CA (US);
Diego Andres Cornejo Barra, Chicago, IL (US);
Stephen Andrew Mcritchie, Palo Alto, CA (US);
Christopher Mark Broadbent, Wellington, FL (US);
Vishal Vishnoi, Redwood City, CA (US);
Srinivasa Phani Kumar Gadde, Fremont, CA (US);
Poorya Zaremoodi, Melbourne, AU;
Thanh Long Duong, Seabrook, AU;
Bhagya Gayathri Hettige, Melbourne, AU;
Tuyen Quang Pham, Springvale, AU;
Arash Shamaei, Kirkland, WA (US);
Thanh Tien Vu, Herston, AU;
Yakupitiyage Don Thanuja Samodhve Dharmasiri, Melbourne, AU;
ORACLE INTERNATIONAL CORPORATION, Redwood Shores, CA (US);
Abstract
Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.